"""simple docstring"""



import numpy as np

SCREAMING_SNAKE_CASE__:int									=			[
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]






class  snake_case__      :


 def __init__( self						):
        __a       =    np.array(lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase						):
        __a ,   __a       =    np.where(letter == self.SQUARE						)
        __a       =    np.concatenate([indexa + 1, indexa + 1]						)
        return indexes


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.SQUARE[indexa - 1, indexa - 1]
        return letter


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    message.lower()
        __a       =    message.replace(" "    ,		""						)
        __a       =    message.replace("j"    ,		"i"						)

        __a       =    np.empty((2, len(lowerCamelCase						))						)
        for letter_index in range(len(lowerCamelCase						)						):
               __a       =    self.letter_to_numbers(message[letter_index]						)

               __a       =    numbers[0]
               __a       =    numbers[1]

        __a       =    first_step.reshape(2 * len(lowerCamelCase						)						)
        __a       =    ""
        for numbers_index in range(len(lowerCamelCase						)						):
               __a       =    int(second_step[numbers_index * 2]						)
               __a       =    int(second_step[(numbers_index * 2) + 1]						)
               __a       =    self.numbers_to_letter(lowerCamelCase    ,		lowerCamelCase						)
               __a       =    encoded_message + letter

        return encoded_message






 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    message.lower()
        message.replace(" "    ,		""						)
        __a       =    np.empty(2 * len(lowerCamelCase						)						)
        for letter_index in range(len(lowerCamelCase						)						):
               __a       =    self.letter_to_numbers(message[letter_index]						)
               __a       =    numbers[0]
               __a       =    numbers[1]

        __a       =    first_step.reshape((2, len(lowerCamelCase						))						)
        __a       =    ""
        for numbers_index in range(len(lowerCamelCase						)						):
               __a       =    int(second_step[0, numbers_index]						)
               __a       =    int(second_step[1, numbers_index]						)
               __a       =    self.numbers_to_letter(lowerCamelCase    ,		lowerCamelCase						)
               __a       =    decoded_message + letter

        return decoded_message

"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__:str									=			HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__:List[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__:Optional[int]									=			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__:Tuple									=			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
